{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "b1143fd5-01e1-494a-88ed-49c9af373ef4",
   "metadata": {},
   "source": [
    "# Converting CO2 densities to pressures to depths\n",
    "- This notebook shows how to use the equation of state of pure CO2 of Span and Wanger (1996) implemented through coolprop to calculate pressures of fluid inclusions\n",
    "- We also show how to use Monte-Carlo simulations to propagate errors when determing fluid inclusion pressures\n",
    "- We also show how to calculate densities to depths using a variety of crustal density models\n",
    "- See the notebook 'Dayton_et_al_2023_LaPalmaExample.ipynb' to see how to propagate uncertainty in these different parameters"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "301242b2",
   "metadata": {},
   "source": [
    "### Install DiadFit if you havent already! You might also have to install CoolProp if you want to use Span and Wagner EOS - the error message will give you instructions, else reach out"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "179b9097",
   "metadata": {},
   "outputs": [],
   "source": [
    "#!pip install --upgrade DiadFit"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4696e294-e9af-4c4e-86e5-ac37ec698a43",
   "metadata": {},
   "source": [
    "## First, import all the python things you need"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "12e26fc7-8a59-4e52-8a16-74766eda628e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'1.0.11'"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import DiadFit as pf\n",
    "pf.__version__"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "de703c96",
   "metadata": {},
   "source": [
    "## Lets just look at a single FI first"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "ca7d1688",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>P_kbar</th>\n",
       "      <th>P_MPa</th>\n",
       "      <th>T_K</th>\n",
       "      <th>CO2_dens_gcm3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6.401596</td>\n",
       "      <td>640.159635</td>\n",
       "      <td>1423.15</td>\n",
       "      <td>0.9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     P_kbar       P_MPa      T_K  CO2_dens_gcm3\n",
       "0  6.401596  640.159635  1423.15            0.9"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "P_kbar=pf.calculate_P_for_rho_T(T_K=1150+273.15,\n",
    "CO2_dens_gcm3=0.9)\n",
    "P_kbar.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "e12022c6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Pressure (kbar)</th>\n",
       "      <th>Pressure (MPa)</th>\n",
       "      <th>Depth (km)</th>\n",
       "      <th>MC_crust_dens_kgm3</th>\n",
       "      <th>model</th>\n",
       "      <th>MC_T_K</th>\n",
       "      <th>MC_CO2_dens_gcm3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6.401596</td>\n",
       "      <td>640.159635</td>\n",
       "      <td>24.168824</td>\n",
       "      <td>2700</td>\n",
       "      <td>None</td>\n",
       "      <td>1423.15</td>\n",
       "      <td>0.9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Pressure (kbar)  Pressure (MPa)  Depth (km)  MC_crust_dens_kgm3 model  \\\n",
       "0         6.401596      640.159635   24.168824                2700  None   \n",
       "\n",
       "    MC_T_K  MC_CO2_dens_gcm3  \n",
       "0  1423.15               0.9  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "D_Constant_rho=pf.convert_co2_dens_press_depth(T_K=1150+273.15,\n",
    "CO2_dens_gcm3=0.9, crust_dens_kgm3=2700, g=9.81, output='df')\n",
    "D_Constant_rho.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f994881b-f307-4e7a-9b3c-6211d4e17e46",
   "metadata": {},
   "source": [
    "## Load your FI densities \n",
    "- In this example, we just load a simple spreadsheet with densities and a sample name. E.g. this could be from microthermometry, or from Raman"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "9cd20b9c-30f6-4250-9f46-b08b21823df1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Sample</th>\n",
       "      <th>Density_g_cm3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>FI1</td>\n",
       "      <td>0.389845</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>FI2</td>\n",
       "      <td>0.923179</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>FI4</td>\n",
       "      <td>0.393791</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>FI5</td>\n",
       "      <td>0.769280</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>FI7</td>\n",
       "      <td>0.741868</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Sample  Density_g_cm3\n",
       "0    FI1       0.389845\n",
       "1    FI2       0.923179\n",
       "2    FI4       0.393791\n",
       "3    FI5       0.769280\n",
       "4    FI7       0.741868"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get from here: https://github.com/PennyWieser/DiadFit/blob/main/docs/Examples/EOS_calculations/Fluid_Inclusion_Densities_Example1.xlsx\n",
    "data=pd.read_excel('Fluid_Inclusion_Densities_Example1.xlsx')\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "962d5645-7951-4c5c-a1de-2b32401dca8f",
   "metadata": {},
   "source": [
    "## Simple conversion to pressure\n",
    "- Most simply, you can convert each density to pressure using the equation of state of Span and Wagner, implemented in CoolProp.\n",
    "- You also need an estimate of the entrapment temperature. Here we use a fixed value, but you could also allocate a temperature based on the forsterite content of your olivine, or any other thermometer suitable for your system\n",
    "- Temperature must be in Kelvin!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "a7b933e5-7f82-4f61-9f67-827caf62a253",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>P_kbar</th>\n",
       "      <th>P_MPa</th>\n",
       "      <th>T_K</th>\n",
       "      <th>CO2_dens_gcm3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.445991</td>\n",
       "      <td>144.599143</td>\n",
       "      <td>1423.15</td>\n",
       "      <td>0.389845</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>6.780995</td>\n",
       "      <td>678.099451</td>\n",
       "      <td>1423.15</td>\n",
       "      <td>0.923179</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.466838</td>\n",
       "      <td>146.683822</td>\n",
       "      <td>1423.15</td>\n",
       "      <td>0.393791</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.570341</td>\n",
       "      <td>457.034146</td>\n",
       "      <td>1423.15</td>\n",
       "      <td>0.769280</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4.246948</td>\n",
       "      <td>424.694773</td>\n",
       "      <td>1423.15</td>\n",
       "      <td>0.741868</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     P_kbar       P_MPa      T_K  CO2_dens_gcm3\n",
       "0  1.445991  144.599143  1423.15       0.389845\n",
       "1  6.780995  678.099451  1423.15       0.923179\n",
       "2  1.466838  146.683822  1423.15       0.393791\n",
       "3  4.570341  457.034146  1423.15       0.769280\n",
       "4  4.246948  424.694773  1423.15       0.741868"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "P_kbar=pf.calculate_P_for_rho_T(T_K=1150+273.15,\n",
    "CO2_dens_gcm3=data['Density_g_cm3'])\n",
    "P_kbar.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "64b4f82b-a9c1-47d6-af2e-a46c2e47cf33",
   "metadata": {},
   "source": [
    "## Convert to Depth in a single step from density\n",
    "- We can also specify a variety of options in this function to directly get depth, instead of pressure"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3b875f26-456d-4e9d-8801-f846556003e2",
   "metadata": {},
   "source": [
    "### Using a constant crustal density of 2700 kg/m$^{3}$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "e43d9a71-d850-4003-85ce-6a284fac636b",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Pressure (kbar)</th>\n",
       "      <th>Pressure (MPa)</th>\n",
       "      <th>Depth (km)</th>\n",
       "      <th>MC_crust_dens_kgm3</th>\n",
       "      <th>model</th>\n",
       "      <th>MC_T_K</th>\n",
       "      <th>MC_CO2_dens_gcm3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.445991</td>\n",
       "      <td>144.599143</td>\n",
       "      <td>5.459250</td>\n",
       "      <td>2700</td>\n",
       "      <td>None</td>\n",
       "      <td>1423.15</td>\n",
       "      <td>0.389845</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>6.780995</td>\n",
       "      <td>678.099451</td>\n",
       "      <td>25.601218</td>\n",
       "      <td>2700</td>\n",
       "      <td>None</td>\n",
       "      <td>1423.15</td>\n",
       "      <td>0.923179</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.466838</td>\n",
       "      <td>146.683822</td>\n",
       "      <td>5.537955</td>\n",
       "      <td>2700</td>\n",
       "      <td>None</td>\n",
       "      <td>1423.15</td>\n",
       "      <td>0.393791</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.570341</td>\n",
       "      <td>457.034146</td>\n",
       "      <td>17.255036</td>\n",
       "      <td>2700</td>\n",
       "      <td>None</td>\n",
       "      <td>1423.15</td>\n",
       "      <td>0.769280</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4.246948</td>\n",
       "      <td>424.694773</td>\n",
       "      <td>16.034084</td>\n",
       "      <td>2700</td>\n",
       "      <td>None</td>\n",
       "      <td>1423.15</td>\n",
       "      <td>0.741868</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Pressure (kbar)  Pressure (MPa)  Depth (km)  MC_crust_dens_kgm3 model  \\\n",
       "0         1.445991      144.599143    5.459250                2700  None   \n",
       "1         6.780995      678.099451   25.601218                2700  None   \n",
       "2         1.466838      146.683822    5.537955                2700  None   \n",
       "3         4.570341      457.034146   17.255036                2700  None   \n",
       "4         4.246948      424.694773   16.034084                2700  None   \n",
       "\n",
       "    MC_T_K  MC_CO2_dens_gcm3  \n",
       "0  1423.15          0.389845  \n",
       "1  1423.15          0.923179  \n",
       "2  1423.15          0.393791  \n",
       "3  1423.15          0.769280  \n",
       "4  1423.15          0.741868  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "D_Constant_rho=pf.convert_co2_dens_press_depth(T_K=1150+273.15,\n",
    "CO2_dens_gcm3=data['Density_g_cm3'], crust_dens_kgm3=2700, g=9.81, output='df')\n",
    "D_Constant_rho.head()"
   ]
  },
  {
   "attachments": {
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"
    }
   },
   "cell_type": "markdown",
   "id": "9f987966-a617-4ae7-a0a9-d9c5d97ea4c8",
   "metadata": {},
   "source": [
    "### Using a two-step crustal density\n",
    "- For this, specify a density above and below a boundary, e.g. volcanic pile vs. oceanic crust in an OIB, or crust vs mantle\n",
    "\n",
    "![2_Step_Density.png](attachment:f26a837f-1432-498e-86a2-315102d4ccaf.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "f07c56a8-7855-415d-8694-966dc7feced1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Pressure (kbar)</th>\n",
       "      <th>Pressure (MPa)</th>\n",
       "      <th>Depth (km)</th>\n",
       "      <th>MC_crust_dens_kgm3</th>\n",
       "      <th>model</th>\n",
       "      <th>MC_T_K</th>\n",
       "      <th>MC_CO2_dens_gcm3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.445991</td>\n",
       "      <td>144.599143</td>\n",
       "      <td>5.579991</td>\n",
       "      <td>None</td>\n",
       "      <td>two-step</td>\n",
       "      <td>1423.15</td>\n",
       "      <td>0.389845</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>6.780995</td>\n",
       "      <td>678.099451</td>\n",
       "      <td>23.707763</td>\n",
       "      <td>None</td>\n",
       "      <td>two-step</td>\n",
       "      <td>1423.15</td>\n",
       "      <td>0.923179</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.466838</td>\n",
       "      <td>146.683822</td>\n",
       "      <td>5.650826</td>\n",
       "      <td>None</td>\n",
       "      <td>two-step</td>\n",
       "      <td>1423.15</td>\n",
       "      <td>0.393791</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.570341</td>\n",
       "      <td>457.034146</td>\n",
       "      <td>16.196199</td>\n",
       "      <td>None</td>\n",
       "      <td>two-step</td>\n",
       "      <td>1423.15</td>\n",
       "      <td>0.769280</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4.246948</td>\n",
       "      <td>424.694773</td>\n",
       "      <td>15.097342</td>\n",
       "      <td>None</td>\n",
       "      <td>two-step</td>\n",
       "      <td>1423.15</td>\n",
       "      <td>0.741868</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Pressure (kbar)  Pressure (MPa)  Depth (km) MC_crust_dens_kgm3     model  \\\n",
       "0         1.445991      144.599143    5.579991               None  two-step   \n",
       "1         6.780995      678.099451   23.707763               None  two-step   \n",
       "2         1.466838      146.683822    5.650826               None  two-step   \n",
       "3         4.570341      457.034146   16.196199               None  two-step   \n",
       "4         4.246948      424.694773   15.097342               None  two-step   \n",
       "\n",
       "    MC_T_K  MC_CO2_dens_gcm3  \n",
       "0  1423.15          0.389845  \n",
       "1  1423.15          0.923179  \n",
       "2  1423.15          0.393791  \n",
       "3  1423.15          0.769280  \n",
       "4  1423.15          0.741868  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "D_2_step=pf.convert_co2_dens_press_depth(T_K=1150+273.15,\n",
    "CO2_dens_gcm3=data['Density_g_cm3'], model='two-step', rho1=2600, rho2=3000, d1=5, g=9.81, output='df')\n",
    "D_2_step.head()"
   ]
  },
  {
   "attachments": {
    "e1b1c46d-ebeb-4707-ba03-e25717632fbc.png": {
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"
    }
   },
   "cell_type": "markdown",
   "id": "ddd89905-cac6-402a-a887-4de2e30a2a99",
   "metadata": {},
   "source": [
    "## Using a 3-step crustal density\n",
    "- Here, we assume a volcanic layer, oceanic crust, and mantle. \n",
    "\n",
    "![3_Step_Density.png](attachment:e1b1c46d-ebeb-4707-ba03-e25717632fbc.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "0ae1a36b-22a3-4001-bf46-c48ce68c4383",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Pressure (kbar)</th>\n",
       "      <th>Pressure (MPa)</th>\n",
       "      <th>Depth (km)</th>\n",
       "      <th>MC_crust_dens_kgm3</th>\n",
       "      <th>model</th>\n",
       "      <th>MC_T_K</th>\n",
       "      <th>MC_CO2_dens_gcm3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.445991</td>\n",
       "      <td>144.599143</td>\n",
       "      <td>5.579991</td>\n",
       "      <td>None</td>\n",
       "      <td>three-step</td>\n",
       "      <td>1423.15</td>\n",
       "      <td>0.389845</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>6.780995</td>\n",
       "      <td>678.099451</td>\n",
       "      <td>22.825239</td>\n",
       "      <td>None</td>\n",
       "      <td>three-step</td>\n",
       "      <td>1423.15</td>\n",
       "      <td>0.923179</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.466838</td>\n",
       "      <td>146.683822</td>\n",
       "      <td>5.650826</td>\n",
       "      <td>None</td>\n",
       "      <td>three-step</td>\n",
       "      <td>1423.15</td>\n",
       "      <td>0.393791</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.570341</td>\n",
       "      <td>457.034146</td>\n",
       "      <td>15.996545</td>\n",
       "      <td>None</td>\n",
       "      <td>three-step</td>\n",
       "      <td>1423.15</td>\n",
       "      <td>0.769280</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4.246948</td>\n",
       "      <td>424.694773</td>\n",
       "      <td>14.997584</td>\n",
       "      <td>None</td>\n",
       "      <td>three-step</td>\n",
       "      <td>1423.15</td>\n",
       "      <td>0.741868</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Pressure (kbar)  Pressure (MPa)  Depth (km) MC_crust_dens_kgm3       model  \\\n",
       "0         1.445991      144.599143    5.579991               None  three-step   \n",
       "1         6.780995      678.099451   22.825239               None  three-step   \n",
       "2         1.466838      146.683822    5.650826               None  three-step   \n",
       "3         4.570341      457.034146   15.996545               None  three-step   \n",
       "4         4.246948      424.694773   14.997584               None  three-step   \n",
       "\n",
       "    MC_T_K  MC_CO2_dens_gcm3  \n",
       "0  1423.15          0.389845  \n",
       "1  1423.15          0.923179  \n",
       "2  1423.15          0.393791  \n",
       "3  1423.15          0.769280  \n",
       "4  1423.15          0.741868  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "D_3_step=pf.convert_co2_dens_press_depth(T_K=1150+273.15,\n",
    "CO2_dens_gcm3=data['Density_g_cm3'], model='three-step', rho1=2600, rho2=3000, rho3=3300,\n",
    "d1=5, d2=14, g=9.81, output='df')\n",
    "D_3_step.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1eb743b3-7690-4b77-bd6f-e888a06ca5b0",
   "metadata": {},
   "source": [
    "## Using published density profiles - get more detail in the docs\n",
    "- We also include the following crustal density profiles\n",
    "- ryan_lerner: update of Ryan 1987 by Lerner et al. 2021 \n",
    "- mavko_debari - Mavko and Thompson (1983) and DeBari and Greene (2011) from Putirka (2017)\n",
    "- hill_zucca - Hill and Zucca (1987)  from Putirka (2017)\n",
    "- prezzi - tweaked for andes, Prezzi et al. (2009) from Putirka (2017)\n",
    "- rasmussen - Rasmussen et al. (2022) for arcs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "2310ca3a-4f1e-474b-897b-77e1c21041ca",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Pressure (kbar)</th>\n",
       "      <th>Pressure (MPa)</th>\n",
       "      <th>Depth (km)</th>\n",
       "      <th>MC_crust_dens_kgm3</th>\n",
       "      <th>model</th>\n",
       "      <th>MC_T_K</th>\n",
       "      <th>MC_CO2_dens_gcm3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.445991</td>\n",
       "      <td>144.599143</td>\n",
       "      <td>5.840007</td>\n",
       "      <td>None</td>\n",
       "      <td>rasmussen</td>\n",
       "      <td>1423.15</td>\n",
       "      <td>0.389845</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>6.780995</td>\n",
       "      <td>678.099451</td>\n",
       "      <td>NaN</td>\n",
       "      <td>None</td>\n",
       "      <td>rasmussen</td>\n",
       "      <td>1423.15</td>\n",
       "      <td>0.923179</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.466838</td>\n",
       "      <td>146.683822</td>\n",
       "      <td>5.920451</td>\n",
       "      <td>None</td>\n",
       "      <td>rasmussen</td>\n",
       "      <td>1423.15</td>\n",
       "      <td>0.393791</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.570341</td>\n",
       "      <td>457.034146</td>\n",
       "      <td>17.520428</td>\n",
       "      <td>None</td>\n",
       "      <td>rasmussen</td>\n",
       "      <td>1423.15</td>\n",
       "      <td>0.769280</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4.246948</td>\n",
       "      <td>424.694773</td>\n",
       "      <td>16.329165</td>\n",
       "      <td>None</td>\n",
       "      <td>rasmussen</td>\n",
       "      <td>1423.15</td>\n",
       "      <td>0.741868</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Pressure (kbar)  Pressure (MPa)  Depth (km) MC_crust_dens_kgm3      model  \\\n",
       "0         1.445991      144.599143    5.840007               None  rasmussen   \n",
       "1         6.780995      678.099451         NaN               None  rasmussen   \n",
       "2         1.466838      146.683822    5.920451               None  rasmussen   \n",
       "3         4.570341      457.034146   17.520428               None  rasmussen   \n",
       "4         4.246948      424.694773   16.329165               None  rasmussen   \n",
       "\n",
       "    MC_T_K  MC_CO2_dens_gcm3  \n",
       "0  1423.15          0.389845  \n",
       "1  1423.15          0.923179  \n",
       "2  1423.15          0.393791  \n",
       "3  1423.15          0.769280  \n",
       "4  1423.15          0.741868  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "D_Ras=pf.convert_co2_dens_press_depth(T_K=1150+273.15,\n",
    "CO2_dens_gcm3=data['Density_g_cm3'], model='rasmussen', output='df')\n",
    "D_Ras.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "513ad4b3-8ae1-4196-8080-a3590639af41",
   "metadata": {},
   "source": [
    "## Plot these up to see the differences"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "ec4572a4-ef06-49ee-bd24-3b1274a5e4a5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Depth (km)')"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(10,5))\n",
    "ax1.plot(D_Ras['MC_CO2_dens_gcm3'], D_Ras['Depth (km)'],\n",
    "         'ok', mfc='red', label='Rasmussen')\n",
    "ax1.plot(D_3_step['MC_CO2_dens_gcm3'], D_3_step['Depth (km)'],\n",
    "         'dk', mfc='yellow', label='3-step')\n",
    "ax1.plot(D_2_step['MC_CO2_dens_gcm3'], D_2_step['Depth (km)'],\n",
    "         'dk', mfc='cyan', label='2-step')\n",
    "ax1.plot(D_Constant_rho['MC_CO2_dens_gcm3'], D_Constant_rho['Depth (km)'],\n",
    "         '.k', mfc='black', label='2700kg/m3')\n",
    "\n",
    "\n",
    "\n",
    "ax2.plot(D_Ras['MC_CO2_dens_gcm3'], D_Ras['Depth (km)'],\n",
    "         'ok', mfc='red', label='Rasmussen')\n",
    "ax2.plot(D_3_step['MC_CO2_dens_gcm3'], D_3_step['Depth (km)'],\n",
    "         'dk', mfc='yellow', label='3-step')\n",
    "ax2.plot(D_2_step['MC_CO2_dens_gcm3'], D_2_step['Depth (km)'],\n",
    "         'dk', mfc='cyan', label='2-step')\n",
    "ax2.plot(D_Constant_rho['MC_CO2_dens_gcm3'], D_Constant_rho['Depth (km)'],\n",
    "         '.k', mfc='black', label='2700kg/m3')\n",
    "ax2.set_xlim([0, 0.3])\n",
    "ax2.set_ylim([0, 5])\n",
    "\n",
    "ax1.legend()\n",
    "ax1.invert_yaxis()\n",
    "ax2.invert_yaxis()\n",
    "\n",
    "\n",
    "ax1.set_xlabel('CO$_2$ density (g/cm$^{3}$)')\n",
    "ax1.set_ylabel('Depth (km)')\n",
    "ax2.set_xlabel('CO$_2$ density (g/cm$^{3}$)')\n",
    "ax2.set_ylabel('Depth (km)')"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "base",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.12.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}